{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notebook for Building a bullet chart in python.\n",
    "Full article posted in http://pbpython.com/bullet-graph.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.ticker import FuncFormatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show examples of using seaborn's palette functionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASwAAABLCAYAAADK+7ojAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18a5a9b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(sns.light_palette(\"green\", 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASwAAABLCAYAAADK+7ojAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18a23a7518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(sns.light_palette(\"red\", 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdMAAABLCAYAAAArvG03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niSkAhMQUAEK/AYavTlXLTu/MAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18a236a470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(sns.light_palette(\"purple\",8, reverse=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the data that we want to plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "limits = [80, 100, 150]\n",
    "data_to_plot = (\"Example 1\", 105, 120)\n",
    "palette = sns.color_palette(\"Blues_r\", len(limits))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try the first version of building a stacked bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZwAAABFCAYAAAB+B1BqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsnDhmJlZFi4cMzPL4v8AZ8gVXgzd6QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18a2327f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.set_aspect('equal')\n",
    "ax.set_yticks([1])\n",
    "ax.set_yticklabels([data_to_plot[0]])\n",
    "\n",
    "prev_limit = 0\n",
    "for idx, lim in enumerate(limits):\n",
    "    ax.barh([1], lim-prev_limit, left=prev_limit, height=15, color=palette[idx])\n",
    "    prev_limit = lim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Expand on the version to add the value we are measuring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 1 artists>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZwAAABFCAYAAAB+B1BqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18a2306550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.set_aspect('equal')\n",
    "ax.set_yticks([1])\n",
    "ax.set_yticklabels([data_to_plot[0]])\n",
    "\n",
    "prev_limit = 0\n",
    "for idx, lim in enumerate(limits):\n",
    "    ax.barh([1], lim-prev_limit, left=prev_limit, height=15, color=palette[idx])\n",
    "    prev_limit = lim\n",
    "    \n",
    "# Draw the value we're measuring\n",
    "ax.barh([1], data_to_plot[1], color='black', height=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now add on the target vertical line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f18a218cdd8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZwAAABFCAYAAAB+B1BqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18a2289828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.set_aspect('equal')\n",
    "ax.set_yticks([1])\n",
    "ax.set_yticklabels([data_to_plot[0]])\n",
    "\n",
    "prev_limit = 0\n",
    "for idx, lim in enumerate(limits):\n",
    "    ax.barh([1], lim-prev_limit, left=prev_limit, height=15, color=palette[idx])\n",
    "    prev_limit = lim\n",
    "    \n",
    "# Draw the value we're measuring\n",
    "ax.barh([1], data_to_plot[1], color='black', height=5)\n",
    "ax.axvline(data_to_plot[2], color=\"gray\", ymin=0.10, ymax=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build out a full function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def bulletgraph(data=None, limits=None, labels=None, axis_label=None, title=None,\n",
    "                size=(5, 3), palette=None, formatter=None, target_color=\"gray\",\n",
    "                bar_color=\"black\", label_color=\"gray\"):\n",
    "    \"\"\" Build out a bullet graph image\n",
    "        Args:\n",
    "            data = List of labels, measures and targets\n",
    "            limits = list of range valules\n",
    "            labels = list of descriptions of the limit ranges\n",
    "            axis_label = string describing x axis\n",
    "            title = string title of plot\n",
    "            size = tuple for plot size\n",
    "            palette = a seaborn palette\n",
    "            formatter = matplotlib formatter object for x axis\n",
    "            target_color = color string for the target line\n",
    "            bar_color = color string for the small bar\n",
    "            label_color = color string for the limit label text\n",
    "        Returns:\n",
    "            a matplotlib figure\n",
    "    \"\"\"\n",
    "    # Determine the max value for adjusting the bar height\n",
    "    # Dividing by 10 seems to work pretty well\n",
    "    h = limits[-1] / 10\n",
    "\n",
    "    # Use the green palette as a sensible default\n",
    "    if palette is None:\n",
    "        palette = sns.light_palette(\"green\", len(limits), reverse=False)\n",
    "\n",
    "    # Must be able to handle one or many data sets via multiple subplots\n",
    "    if len(data) == 1:\n",
    "        fig, ax = plt.subplots(figsize=size, sharex=True)\n",
    "    else:\n",
    "        fig, axarr = plt.subplots(len(data), figsize=size, sharex=True)\n",
    "\n",
    "    # Add each bullet graph bar to a subplot\n",
    "    for idx, item in enumerate(data):\n",
    "\n",
    "        # Get the axis from the array of axes returned when the plot is created\n",
    "        if len(data) > 1:\n",
    "            ax = axarr[idx]\n",
    "\n",
    "        # Formatting to get rid of extra marking clutter\n",
    "        ax.set_aspect('equal')\n",
    "        ax.set_yticklabels([item[0]])\n",
    "        ax.set_yticks([1])\n",
    "        ax.spines['bottom'].set_visible(False)\n",
    "        ax.spines['top'].set_visible(False)\n",
    "        ax.spines['right'].set_visible(False)\n",
    "        ax.spines['left'].set_visible(False)\n",
    "\n",
    "        prev_limit = 0\n",
    "        for idx2, lim in enumerate(limits):\n",
    "            # Draw the bar\n",
    "            ax.barh([1], lim - prev_limit, left=prev_limit, height=h,\n",
    "                    color=palette[idx2])\n",
    "            prev_limit = lim\n",
    "        rects = ax.patches\n",
    "        # The last item in the list is the value we're measuring\n",
    "        # Draw the value we're measuring\n",
    "        ax.barh([1], item[1], height=(h / 3), color=bar_color)\n",
    "\n",
    "        # Need the ymin and max in order to make sure the target marker\n",
    "        # fits\n",
    "        ymin, ymax = ax.get_ylim()\n",
    "        ax.vlines(\n",
    "            item[2], ymin * .9, ymax * .9, linewidth=1.5, color=target_color)\n",
    "\n",
    "    # Now make some labels\n",
    "    if labels is not None:\n",
    "        for rect, label in zip(rects, labels):\n",
    "            height = rect.get_height()\n",
    "            ax.text(\n",
    "                rect.get_x() + rect.get_width() / 2,\n",
    "                -height * .4,\n",
    "                label,\n",
    "                ha='center',\n",
    "                va='bottom',\n",
    "                color=label_color)\n",
    "    if formatter:\n",
    "        ax.xaxis.set_major_formatter(formatter)\n",
    "    if axis_label:\n",
    "        ax.set_xlabel(axis_label)\n",
    "    if title:\n",
    "        fig.suptitle(title, fontsize=14)\n",
    "    fig.subplots_adjust(hspace=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IBPhQ/0Y/D9gQeGS/7uqquqC/fx4wLcmawFOB7z2UV1h1Acd4CvCi/v43gI8NrPthVc0D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\npGYMIpIkqRmDiCRJaub/A1waEC+RWnt0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18a22554e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_to_plot2 = [(\"John Smith\", 105, 120),\n",
    "                 (\"Jane Jones\", 99, 110),\n",
    "                 (\"Fred Flintstone\", 109, 125),\n",
    "                 (\"Barney Rubble\", 135, 123),\n",
    "                 (\"Mr T\", 45, 105)]\n",
    "\n",
    "bulletgraph(data_to_plot2, limits=[20, 60, 100, 160], labels=[\"Poor\", \"OK\", \"Good\", \"Excellent\"], size=(8,5), \n",
    "            axis_label=\"Performance Measure\", label_color=\"black\", bar_color=\"#252525\", target_color='#f7f7f7',\n",
    "            title=\"Sales Rep Performance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def money(x, pos):\n",
    "    'The two args are the value and tick position'\n",
    "    return \"${:,.0f}\".format(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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c5UUWkiSpo2lTh2hTSt0amfYKMKBqWp/y7tuV81JKP664fxtw29popyRJUluW8wieJElS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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18a2052c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "money_fmt = FuncFormatter(money)\n",
    "data_to_plot3 = [(\"Print\", 50000, 60000),\n",
    "                 (\"Billboards\", 75000, 65000),\n",
    "                 (\"Radio\", 125000, 80000),\n",
    "                 (\"Online\", 195000, 115000)]\n",
    "palette = sns.light_palette(\"grey\", 3, reverse=False)\n",
    "bulletgraph(data_to_plot3, limits=[50000, 125000, 200000], labels=[\"Below\", \"On Target\", \"Above\"], size=(10,5), \n",
    "            axis_label=\"Annual Budget\", label_color=\"black\", bar_color=\"#252525\", target_color='#f7f7f7', palette=palette,\n",
    "            title=\"Marketing Channel Budget Performance\", formatter=money_fmt)"
   ]
  }
 ],
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